Kubernetes Jobs in Kansas (NOW HIRING)

SRE DevOps Engineer

Location: Overland Park, KS / Atlanta, GA / Frisco, TX (Onsite)

Requirements

• 4–9 years in SRE/DevOps/Systems Engineering as Senior or Principal Engineer
• Strong hands‑on experience with Kubernetes, container orchestration, and API management.
• Working knowledge of WAFs, networking security, and database technologies (SQL/NoSQL).
• Proficient in automation and scripting (Python, Go, Ansible, Terraform, etc.)
• Strong observability/monitoring experience.
• Experience with CI/CD pipelines, GitOps, and infrastructure as code.
• Solid problem‑solving and collaboration skills.

Job Responsibilities

• Resolve escalated incidents across Kubernetes, API Proxy, WAF, DBs, and infra platforms.
• Design and improve runbooks, automating manual steps wherever possible.
• Lead and contribute to building self‑healing systems and self‑service tooling for users.
• Analyze incident trends, propose improvements in monitoring, capacity, and reliability.
• Collaborate with engineering teams on deployment, upgrades, and performance optimization.
• Conduct postmortems, document RCA, and ensure learning is captured.
• Mentor and coach L1 engineers.

SkillsMandatory Skills (Must-Have)

1. Advanced Incident Troubleshooting & Resolution

   Expectation: Diagnose and resolve escalated incidents that L1 cannot handle, often across multiple layers (infrastructure, application, network).

   Example: For an API outage, identify if the root cause is in Kubernetes pod networking, APIgateway misconfig, or backend DB latency — and apply fixes.

2. Kubernetes & Container Orchestration Expertise

   Expectation: Comfortable with deployments, scaling, networking, and debugging cluster‑level issues.

   Example: Troubleshoot why pods are pending by checking node capacity, taints/tolerations, and cluster autoscaler logs.

3. Automation & Scripting (Python, Go, Bash, Ansible, Terraform)

   Expectation: Write scripts and automation to reduce manual toil, enhance monitoring, and improve incident resolution speed.

   Example: Develop a Python script to automatically collect pod and system logs when a service crashes.

4. Observability & Monitoring Tooling

   Expectation: Deep understanding of monitoring, alerting, tracing, and logging systems.

   Example: Build Prometheus alert rules to detect DB query spikes; configure Grafana dashboards for API latency.

5. CI/CD & Infrastructure as Code (IaC)

   Expectation: Familiarity with GitOps workflows, CI/CD pipelines, and infrastructure provisioning.

   Example: Enhance Jenkins pipeline to add automated smoke tests before promoting Kubernetes deployments.

6. Database Troubleshooting (SQL & NoSQL)

   Expectation: Identify performance bottlenecks, connection issues, and basic tuning opportunities.

   Example: Run queries to detect slow‑running SQL statements causing latency in an application.

7. Incident Management & RCA

   Expectation: Act as incident commander for escalated issues, lead bridge calls, and produce Root Cause Analyses.

   Example: After a WAF misconfiguration causes downtime, lead the investigation, document the timeline, and propose preventive actions.

8. Mentorship & Runbook Improvement

   Expectation: Coach L1 engineers, refine runbooks, and introduce new automated workflows.

   Example: Update a runbook to add automated Kubernetes log collection instead of manual steps.

Preferred Skills (Nice-to-Have)

1. Cloud Platform Engineering (AWS, Azure, GCP)

   Expectation: Hands‑on skills in provisioning, scaling, and securing cloud workloads.

   Example: Diagnose why an AWS ALB is misrouting traffic after a deployment.

2. Security & WAF Management

   Expectation: Understand WAF rules, common attacks (SQL injection, XSS), and how to apply fixes.

   Example: Investigate false positives in WAF logs and adjust rule sets with security teams.

3. Capacity & Performance Engineering

   Expectation: Anticipate scaling needs, tune resource utilization, and propose optimizations.

   Example: Identify that a Kubernetes deployment is CPU‑throttled and adjust HPA (Horizontal Pod Autoscaler) configs.

4. Automation Platform Integration (AIOps, ChatOps)

   Expectation: Integrate AI/ML‑powered tools for anomaly detection and auto‑remediation.

   Example: Implement a ChatOps bot that runs predefined Kubernetes troubleshooting commands in Slack.

5. Cross‑Platform Expertise (Hybrid Infra)

   Expectation: Experience supporting both on‑prem and cloud environments seamlessly.

   Example: Compare latency patterns between on‑prem DBs and cloud‑hosted APIs to identify bottlenecks.